Tiered storage of data in a storage network

ABSTRACT

Apparatus for tiered storage of data in a storage network. In an example of operation, a computing device receives a data object for storage and forwards the data object for storage in a first plurality of memory devices of a first memory type. The computing device determines a system level storage efficiency for the data object based, at least in part, on a data attribute associated with the data object. The computing device further selects, based at least in part on the system level storage efficiency preference, a second plurality of memory devices comprised of a second memory type. The computing device determines error encoding parameters based on the second plurality of memory devices, retrieves the data object from the first plurality of memory devices, and encodes the data object with the error encoding parameters to generate a plurality of encoded data slices for storage in the second plurality of memory devices.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 120 as a continuation of U.S. Utility patent applicationSer. No. 17/806,662, entitled “SELECTION OF MEMORY FOR DATA STORAGE IN ASTORAGE NETWORK,” filed Jun. 13, 2022, which is a continuation of U.S.Utility patent application Ser. No. 17/151,249, entitled “SELECTION OFMEMORY IN A DISTRIBUTED DATA STORAGE NETWORK,” filed Jan. 18, 2021,issued as U.S. Pat. No. 11,360,852 on Jun. 14, 2022, which is acontinuation of U.S. Utility patent application Ser. No. 16/580,379,entitled “READ OPTIMIZED AND WRITE OPTIMIZED DS PROCESSING UNITS,” filedSep. 24, 2019, abandoned, which is a continuation-in-part of U.S.Utility patent application Ser. No. 16/047,942, entitled “NAMESPACEAFFINITY AND FAILOVER FOR PROCESSING UNITS IN A DISPERSED STORAGENETWORK,” filed Jul. 27, 2018, abandoned, which is acontinuation-in-part of U.S. Utility patent application Ser. No.15/224,839, entitled “NON-TEMPORARILY STORING TEMPORARILY STORED DATA INA DISPERSED STORAGE NETWORK,” filed Aug. 1, 2016, issued as U.S. Pat.No. 10,102,068 on Oct. 16, 2018, which is a continuation of U.S. Utilityapplication Ser. No. 14/792,898, entitled “NON-TEMPORARILY STORINGTEMPORARILY STORED DATA IN A DISPERSED STORAGE NETWORK,” filed Jul. 7,2015, issued as U.S. Pat. No. 9,407,292 on Aug. 2, 2016, which is acontinuation of U.S. Utility application Ser. No. 13/889,557, entitled“NON-TEMPORARILY STORING TEMPORARILY STORED DATA IN A DISPERSED STORAGENETWORK,” filed May 8, 2013, issued as U.S. Pat. No. 9,110,833 on Aug.18, 2015, which claims priority pursuant to 35 U.S.C. § 119(e) to U.S.Provisional Application No. 61/663,836, entitled “LOAD BALANCING ACCESSOF A DISTRIBUTED STORAGE AND TASK NETWORK,” filed Jun. 25, 2012, each ofwhich is hereby incorporated herein by reference in its entirety andmade part of the present U.S. Utility Patent Application for allpurposes.

BACKGROUND Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to selection of memory for distributed storage of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), workstations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc., on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present disclosure;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present disclosure;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present disclosure;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent disclosure;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present disclosure;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present disclosure;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present disclosure;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent disclosure;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present disclosure;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present disclosure;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present disclosure;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present disclosure;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent disclosure;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present disclosure;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present disclosure;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present disclosure;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent disclosure;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present disclosure;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present disclosure;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present disclosure;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present disclosure;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentdisclosure;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present disclosure;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentdisclosure;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present disclosure;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentdisclosure;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentdisclosure;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent disclosure;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present disclosure;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentdisclosure;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentdisclosure;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present disclosure;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present disclosure;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentdisclosure;

FIG. 40A is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present disclosure;

FIG. 40B is a schematic block diagram of an embodiment of a dispersedstorage network system in accordance with the present disclosure;

FIG. 40C is a flowchart illustrating an example of non-temporarilystoring temporarily stored data in accordance with the presentdisclosure;

FIG. 41A is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present disclosure;

FIG. 41B is a diagram illustrating an example of an affinity table inaccordance with the present disclosure;

FIG. 41C is a flowchart illustrating an example of load-balancing inaccordance with the present disclosure;

FIG. 42 is a flowchart illustrating another example of load-balancing inaccordance with the present disclosure;

FIG. 43A is a schematic diagram illustrating another example ofload-balancing in accordance with the present disclosure;

FIG. 43B is a schematic diagram illustrating another example ofload-balancing in accordance with the present disclosure;

FIG. 43C is a flowchart illustrating another example of load-balancingin accordance with the present disclosure;

FIG. 44A is a schematic block diagram of another embodiment of adistributed storage and task client module in accordance with thepresent disclosure; and

FIG. 44B is a schematic block diagram of another embodiment of adispersed storage network system in accordance with the presentdisclosure; and

FIG. 44C is a flowchart illustrating an example of storing data inaccordance with the present disclosure.

DETAILED DESCRIPTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16 (DSprocessing unit), a distributed storage and/or task network (DSTN)managing unit 18 (DSN managing unit), a DST integrity processing unit 20(DS integrity processing unit), and a distributed storage and/or tasknetwork (DSTN) module 22 (DSN module). The components of the distributedcomputing system 10 are coupled via a network 24, which may include oneor more wireless and/or wire lined communication systems; one or moreprivate intranet systems and/or public internet systems; and/or one ormore local area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 (storage units) that may be located atgeographically different sites (e.g., one in Chicago, one in Milwaukee,etc.). Each of the DST execution units is operable to store dispersederror encoded data and/or to execute, in a distributed manner, one ormore tasks on data. The tasks may be a simple function (e.g., amathematical function, a logic function, an identify function, a findfunction, a search engine function, a replace function, etc.), a complexfunction (e.g., compression, human and/or computer language translation,text-to-voice conversion, voice-to-text conversion, etc.), multiplesimple and/or complex functions, one or more algorithms, one or moreapplications, etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interface 30supports a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general, and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26 ), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations include authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19 ).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39 . With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1 . Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1 ), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1 ), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terabytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1 . For example, the outboundDST processing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units send, via the network 24, their partial results 102to the inbound DST processing section 82 of the DST client module 34.The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terabytes) into 100,000 data segments, each being 1 Gigabyte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the grouping selector modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The grouping selector module 114 outputs the slicegroupings 96 to the corresponding DST execution units 36 via the network24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, where in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phrase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy check(CRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of encodeddata segments 156 for a given data partition, the slicing module outputsa plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45), receives segmenting information (i.e.,control information 160) from a control module, and segments the datapartition 120 in accordance with the control information 160 to producedata segments 152. Each data block may be of the same size as other datablocks or of a different size. In addition, the size of each data blockmay be a few bytes to megabytes of data. As previously mentioned, thesegmenting information indicates how many rows to segment the datapartition into, indicates how many columns to segment the data partitioninto, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7 . In this example, data segment 1 includes 3 rows with eachrow being treated as one word for encoding. As such, data segment 1includes three words for encoding: word 1 including data blocks d1 andd2, word 2 including data blocks d16 and d17, and word 3 including datablocks d31 and d32. Each of data segments 2-7 includes three words whereeach word includes two data blocks. Data segment 8 includes three wordswhere each word includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d 1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice (DS1_d16&17) of the first set of encoded data slices is substantially similarto content of the second word (e.g., d16 & d17); and the content of thethird encoded data slice (DS1_d 31&32) of the first set of encoded dataslices is substantially similar to content of the third word (e.g., d31& d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the first set of encoded data slices include error correctiondata based on the first-third words of the first data segment. With suchan encoding and slicing scheme, retrieving any three of the five encodeddata slices allows the data segment to be accurately reconstructed.

The encoding and slicing of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d 3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4); the content of the second encoded data slice (DS2_d 18&19) of thesecond set of encoded data slices is substantially similar to content ofthe second word (e.g., d18 & d19); and the content of the third encodeddata slice (DS2_d 33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 & d34).The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the second set of encoded data slices includes errorcorrection data based on the first-third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. Encoded slices for data partition 122 are grouped inaccordance with the control information 160 to produce slice groupings96. In this example, a grouping selector module 114 organizes theencoded data slices into five slice groupings (e.g., one for each DSTexecution unit of a distributed storage and task network (DSTN) module).As a specific example, the grouping selector module 114 creates a firstslice grouping for a DST execution unit #1, which includes first encodedslices of each of the sets of encoded slices. As such, the first DSTexecution unit receives encoded data slices corresponding to data blocks1-15 (e.g., encoded data slices of contiguous data).

The grouping selector module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selector module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selector module 114 creates a fourth slice grouping for DSTexecution unit #4, which includes fourth encoded slices of each of thesets of encoded slices. As such, the fourth DST execution unit receivesencoded data slices corresponding to first error encoding information(e.g., encoded data slices of error coding (EC) data). The groupingselector module 114 further creates a fifth slice grouping for DSTexecution unit #5, which includes fifth encoded slices of each of thesets of encoded slices. As such, the fifth DST execution unit receivesencoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9 ), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_3) is sent to the fourth DST execution unit; the fourth slice groupingof the second data partition (e.g., slice group 2_4, which includesfirst error coding information) is sent to the fifth DST execution unit;and the fifth slice grouping of the second data partition (e.g., slicegroup 2_5, which includes second error coding information) is sent tothe first DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may vary in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task 94 is dividedinto partial tasks that are sent to the DST execution units inconjunction with the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9 . The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or anyother analog and/or digital processing circuitry), availability of theprocessing resources, etc. If the controller 86 determines that the DTexecution module(s) 90 have sufficient capabilities, it generates taskcontrol information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identify other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 in thememory 88. For example, when the partial task 98 includes a retrievalrequest, the controller 86 outputs the memory control 174 to the memory88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step of executing the task in accordance with the taskcontrol information 176, the DT execution module 90 retrieves theencoded slices from memory 88. The DT execution module 90 thenreconstructs contiguous data blocks of a data partition. As shown forthis example, reconstructed contiguous data blocks of data partition 1include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbound DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task was to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieved slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, results of processing theinformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9 .Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., retrievedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, un-secures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6 ) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRCverification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8 , an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d 1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d1 and d2, word 2 including data blocks d16and d17, and word 3 including data blocks d31 and d32. Each of datasegments 2-7 includes three words where each word includes two datablocks. Data segment 8 includes three words where each word includes asingle data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10 .

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23 .The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24 .

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupingselector module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC, etc.)), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC,etc.)). The control information 160 indicates which steps of the DSerror encoding are active for the data 92 and, for active steps,indicates the parameters for the step. For example, the controlinformation 160 indicates that the error encoding is active and includeserror encoding parameters (e.g., pillar width, decode threshold, writethreshold, read threshold, type of error encoding, etc.).

The group selector module 114 groups the encoded slices 218 of the datasegments into pillars of slices 216. The number of pillars correspondsto the pillar width of the DS error encoding parameters. In thisexample, the distributed task control module 118 facilitates the storagerequest.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21 . TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selector module organizes the sets of encoded dataslices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selector module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selector module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, and a de-segmenting processing module 210. Thedispersed error decoding module 182 is operable to de-slice and decodeencoded slices per data segment 218 utilizing a de-slicing and decodingfunction 228 to produce a plurality of data segments that arede-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6 ) received as controlinformation 190 from a control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC verification, etc.), and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from a controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Solomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, un-secures the secured data segments based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19 .

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39 . In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distribution module's location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39 .

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28 . The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task⇔sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few Terabytes or more),addressing information of Addr_1_AA, and DS parameters of 3/5; SEG_1;and SLC_1. In this example, the addressing information may be a virtualaddress corresponding to the virtual address of the first storage word(e.g., one or more bytes) of the data and information on how tocalculate the other addresses, may be a range of virtual addresses forthe storage words of the data, physical addresses of the first storageword or the storage words of the data, may be a list of slice names ofthe encoded data slices of the data, etc. The DS parameters may includeidentity of an error encoding scheme, decode threshold/pillar width(e.g., 3/5 for the first data entry), segment security information(e.g., SEG_1), per slice security information (e.g., SLC_1), and/or anyother information regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_2_XY, and DSparameters of 3/5; SEG_2; and SLC_2. In this example, the addressinginformation may be a virtual address corresponding to the virtualaddress of the first storage word (e.g., one or more bytes) of the taskand information on how to calculate the other addresses, may be a rangeof virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slice names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., 3/5 for the first data entry),segment security information (e.g., SEG_2), per slice securityinformation (e.g., SLC_2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task⇔sub-task mapping information table 246 includes a task field256 and a sub-task field 258. The task field 256 identifies a taskstored in the memory of a distributed storage and task network (DSTN)module and the corresponding sub-task fields 258 indicates whether thetask includes sub-tasks and, if so, how many and if any of the sub-tasksare ordered. In this example, the task⇔sub-task mapping informationtable 246 includes an entry for each task stored in memory of the DSTNmodule (e.g., task 1 through task k). In particular, this exampleindicates that task 1 includes 7 sub-tasks; task 2 does not includesub-tasks, and task k includes r number of sub-tasks (where r is aninteger greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280includes identity of the capabilities of the corresponding DT executionunit. For example, DT execution module 1_1 includes capabilities X,where X includes one or more of MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.), and/or any information germane toexecuting one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words and/or phrasesin translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identifynon-words (non-ordered); task 1_2—identify unique words (non-ordered);task 1_3—translate (non-ordered); task 1_4—translate back (ordered aftertask 1_3); task 1_5—compare to ID errors (ordered after task 1-4); task1_6—determine non-word translation errors (ordered after task 1_5 and1_1); and task 1_7—determine correct translations (ordered after 1_5 and1_2). The sub-task further indicates whether they are an ordered task(i.e., are dependent on the outcome of another task) or non-order (i.e.,are independent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_1 translate; andtask 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases 288. The translated data 282 istranslated back 308 (e.g., sub-task 1_4) into the language of theoriginal data to produce re-translated data 284. These two tasks aredependent on the translate task (e.g., task 1_3) and thus must beordered after the translation task, which may be in a pipelined orderingor a serial ordering. The re-translated data 284 is then compared 310with the original data 92 to find words and/or phrases that did nottranslate (one way and/or the other) properly to produce a list ofincorrectly translated words 294. As such, the comparing task (e.g.,sub-task 1_5) 310 is ordered after the translation 306 andre-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.,) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 . As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29 , the respective data/task has DSparameters of 3/5 for their decode threshold/pillar width; hencespanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30 . The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30 , where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done by the DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_1 through 2_z) and addressing information foreach partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_1 (e.g., identify non-words on the data) has no task ordering (i.e.,is independent of the results of other sub-tasks), is to be performed ondata partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1,4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and5_1 search for non-words in data partitions 2_1 through 2_z to producetask 1_1 intermediate results (R1-1, which is a list of non-words). Task1_2 (e.g., identify unique words) has similar task execution informationas task 1_1 to produce task 1_2 intermediate results (R1-2, which is thelist of unique words).

Task 1_3 (e.g., translate) includes task execution information as beingnon-ordered (i.e., is independent), having DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 andhaving DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate datapartitions 2_5 through 2_z to produce task 1_3 intermediate results(R1-3, which is the translated data). In this example, the datapartitions are grouped, where different sets of DT execution modulesperform a distributed sub-task (or task) on each data partition group,which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to beexecuted on task 1_3's intermediate result (e.g., R1-3_1) (e.g., thetranslated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 areallocated to translate back task 1_3 intermediate result partitionsR1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and7_2 are allocated to translate back task 1_3 intermediate resultpartitions R1-3_5 through R1-3_z to produce task 1-4 intermediateresults (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translationerrors) is ordered after task 1_4 and is to be executed on task 1_4'sintermediate results (R4-1) and on the data. DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1through 2_z) with partitions of task 1-4 intermediate results partitionsR1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5,which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered aftertasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_6 intermediate results (R1-6, which is the list translation errors dueto non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered aftertasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_7 intermediate results (R1-7, which is the list of correctlytranslated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_1 through 2_z by DT execution modules3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1,4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in datapartitions 2_1 through 2_z to produce task 2 intermediate results (R2,which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) isordered after task 1_3 (e.g., translate) is to be performed onpartitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2,4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and5_2 search for specific translated words and/or phrases in thepartitions of the translated data (R1-3_1 through R1-3_z) to producetask 3_2 intermediate results (R3-2, which is a list of specifictranslated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_1), DST unit 1 isresponsible for overseeing execution of the task 1_1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30 . In FIG.33 , the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information ofFIG. 32 ) executes task 1_1 to produce a first partial result 102 ofnon-words found in the first data partition. The second set of DTexecution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DSTallocation information of FIG. 32 ) executes task 1_1 to produce asecond partial result 102 of non-words found in the second datapartition. The sets of DT execution modules (as per the DST allocationinformation) perform task 1_1 on the data partitions until the “z” setof DT execution modules performs task 1_1 on the “zth” data partition toproduce a “zth” partial result 102 of non-words found in the “zth” datapartition.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 1 is assigned to process the first through “zth” partialresults to produce the first intermediate result (R1-1), which is a listof non-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the first intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of non-words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the firstintermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1through R1-1_m). If the first intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34 , the DSTN module is performing task 1_2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_1 if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_2 inaccordance with the DST allocation information. From data partition todata partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 1 is assigned to process the first through “zth” partialresults 102 of task 1_2 to produce the second intermediate result(R1-2), which is a list of unique words found in the data 92. Theprocessing module of DST execution 1 is engaged to aggregate the firstthrough “zth” partial results of unique words to produce the secondintermediate result. The processing module stores the secondintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the second intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of unique words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the secondintermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1through R1-2_m). If the second intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35 , the DSTN module is performing task 1_3 (e.g., translate) onthe data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task 1_1if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_3 inaccordance with the DST allocation information (e.g., DT executionmodules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2translate data partitions 2_5 through 2_z). For the data partitions, theallocated set of DT execution modules 90 executes task 1_3 to producepartial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 2 is assigned to process the first through “zth” partialresults of task 1_3 to produce the third intermediate result (R1-3),which is translated data. The processing module of DST execution 2 isengaged to aggregate the first through “zth” partial results oftranslated data to produce the third intermediate result. The processingmodule stores the third intermediate result as non-DS error encoded datain the scratchpad memory or in another section of memory of DSTexecution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the third intermediate result (e.g., translateddata). To begin the encoding, the DST client module partitions the thirdintermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1through R1-3_y). For each partition of the third intermediate result,the DST client module uses the DS error encoding parameters of the data(e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 2-6 per the DST allocationinformation).

As is further shown in FIG. 35 , the DSTN module is performing task 1_4(e.g., retranslate) on the translated data of the third intermediateresult. To begin, the DSTN module accesses the translated data (from thescratchpad memory or from the intermediate result memory and decodes it)and partitions it into a plurality of partitions in accordance with theDST allocation information. For each partition of the third intermediateresult, the DSTN identifies a set of its DT execution modules 90 toperform task 1_4 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated totranslate back partitions R1-3_1 through R1-3_4 and DT execution modules1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitionsR1-3_5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_4 to produce partial results 102(e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 3 is assigned to process the first through “zth” partialresults of task 1_4 to produce the fourth intermediate result (R1-4),which is retranslated data. The processing module of DST execution 3 isengaged to aggregate the first through “zth” partial results ofretranslated data to produce the fourth intermediate result. Theprocessing module stores the fourth intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the fourth intermediate result (e.g., retranslateddata). To begin the encoding, the DST client module partitions thefourth intermediate result (R1-4) into a plurality of partitions (e.g.,R1-4_1 through R1-4_z). For each partition of the fourth intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 3-7 per the DST allocationinformation).

In FIG. 36 , a distributed storage and task network (DSTN) module isperforming task 1_5 (e.g., compare) on data 92 and retranslated data ofFIG. 35 . To begin, the DSTN module accesses the data 92 and partitionsit into a plurality of partitions in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. The DSTN module also accesses the retranslateddata from the scratchpad memory, or from the intermediate result memoryand decodes it, and partitions it into a plurality of partitions inaccordance with the DST allocation information. The number of partitionsof the retranslated data corresponds to the number of partitions of thedata.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_5 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 1 is assigned to process the first through “zth” partialresults of task 1_5 to produce the fifth intermediate result (R1-5),which is the list of incorrectly translated words and/or phrases. Inparticular, the processing module of DST execution 1 is engaged toaggregate the first through “zth” partial results of the list ofincorrectly translated words and/or phrases to produce the fifthintermediate result. The processing module stores the fifth intermediateresult as non-DS error encoded data in the scratchpad memory or inanother section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the fifth intermediate result. To begin theencoding, the DST client module partitions the fifth intermediate result(R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5_z). Foreach partition of the fifth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36 , the DSTN module is performing task 1_6(e.g., translation errors due to non-words) on the list of incorrectlytranslated words and/or phrases (e.g., the fifth intermediate resultR1-5) and the list of non-words (e.g., the first intermediate resultR1-1). To begin, the DSTN module accesses the lists and partitions theminto a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_6 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 2 is assigned to process the first through “zth” partialresults of task 1_6 to produce the sixth intermediate result (R1-6),which is the list of incorrectly translated words and/or phrases due tonon-words. In particular, the processing module of DST execution 2 isengaged to aggregate the first through “zth” partial results of the listof incorrectly translated words and/or phrases due to non-words toproduce the sixth intermediate result. The processing module stores thesixth intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the sixth intermediate result. To begin theencoding, the DST client module partitions the sixth intermediate result(R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6_z). Foreach partition of the sixth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36 , the DSTN module is performingtask 1_7 (e.g., correctly translated words and/or phrases) on the listof incorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_7 in accordance with the DST allocation information(e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pairof partitions, the allocated set of DT execution modules executes task1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 3 is assigned to process the first through “zth” partialresults of task 1_7 to produce the seventh intermediate result (R1-7),which is the list of correctly translated words and/or phrases. Inparticular, the processing module of DST execution 3 is engaged toaggregate the first through “zth” partial results of the list ofcorrectly translated words and/or phrases to produce the seventhintermediate result. The processing module stores the seventhintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the seventh intermediate result. To begin theencoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 3-7 per the DST allocation information).

In FIG. 37 , the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 7 is assigned to process the first through “zth” partialresults of task 2 to produce task 2 intermediate result (R2), which is alist of specific words and/or phrases found in the data. The processingmodule of DST execution 7 is engaged to aggregate the first through“zth” partial results of specific words and/or phrases to produce thetask 2 intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice groupingbased DS error encode the task 2 intermediate result. To begin theencoding, the DST client module determines whether the list of specificwords and/or phrases is of a sufficient size to partition (e.g., greaterthan a Terabyte). If yes, it partitions the task 2 intermediate result(R2) into a plurality of partitions (e.g., R2_1 through R2_m). If thetask 2 intermediate result is not of sufficient size to partition, it isnot partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38 , the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32 , DSTexecution unit 5 is assigned to process the first through “zth” partialresults of task 3 to produce task 3 intermediate result (R3), which is alist of specific translated words and/or phrases found in the translateddata. In particular, the processing module of DST execution 5 is engagedto aggregate the first through “zth” partial results of specifictranslated words and/or phrases to produce the task 3 intermediateresult. The processing module stores the task 3 intermediate result asnon-DS error encoded data in the scratchpad memory or in another sectionof memory of DST execution unit 7.

DST execution unit 5 engages its DST client module to slice groupingbased DS error encode the task 3 intermediate result. To begin theencoding, the DST client module determines whether the list of specifictranslated words and/or phrases is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the task 3intermediate result (R3) into a plurality of partitions (e.g., R3_1through R3_m). If the task 3 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30 . In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40A is a schematic block diagram of another embodiment of adistributed computing system that includes a data processor 350, adistributed storage and task (DST) client module 34, and a distributedstorage and task network (DSTN) module 22. The DSTN module 22 includes aplurality of DST execution (EX) units 36. The data processor 350 and/orthe DST client module 34 may be implemented in one or more of acomputing device, a server, a user device, a DST processing unit 16, anda DST execution and 36. The data processor 350 and the DST client module34 are operable to encode data 352 into primary slices 356 (e.g., firstencoded data slices) and secondary slices 358 (e.g., second encoded dataslices) for storage in the DSTN module 22.

The DST client module 34 encodes the data 352 utilizing a dispersedstorage error coding function in accordance with a first set (e.g.,temporary) of dispersed storage error coding function parameters toproduce one or more sets of primary slices. The DST client module 34stores the one or more sets of primary slices 356 in the DSTN module 22(e.g., in a temporary memory portion of the DSTN module 22). The dataprocessor 350 processes the data 352 in accordance with a processingfunction to produce processed data 354. The processing function includesat least one of a picture size reduction function, a picture resolutionreduction function, a lower resolution encoding function, a compressionalgorithm, an expansion algorithm, and an encoding function. Forexample, the data processor 350 encodes the data 352 utilizing anencoding function that produces the processed data 354 such that a sizeof the processed data 354 is less than a size of the data 352.

The DST client module 34 encodes the processed data 354 utilizing thedispersed storage error coding function in accordance with a second set(e.g., non-temporary) of dispersed storage error coding functionparameters to produce one or more sets of secondary slices 358. Forexample, the DST client module 34 selects the second set of dispersedstorage error coding function parameters such that a resultingreliability level of storage of secondary slices 358 is lower than aresulting reliability level of storage of the primary slices 356. TheDST client module 34 stores the one or more sets of secondary slices 358of the DSTN module 22 (e.g., in a non-temporary memory portion of theDSTN module 22). The storing includes selecting a set of DST executionunits 36 in accordance with a storage goal. For example, the DST clientmodule 34 selects a first set of DST execution units 36 associated witha lower than average reliability level when the storage goal indicateslower reliability is allowable.

The DST client module 34 rebuilds the secondary slices 358 when a sliceerror is detected of the one or more secondary slices 358. Therebuilding may include prioritization of steps of a rebuilding processin accordance with the storage goal. For example, the DST client module34 rebuilds a secondary slice 358 with a de-prioritized rebuildingschedule when the storage goal indicates that the prioritized rebuildingis allowable.

The DST client module 34 may determine to delete one or more of the oneor more sets of primary slices 356 when deletion is indicated. Theindication of deletion includes at least one of receiving a deleterequest, determining that the one or more primary slices are no longerrequired, and a time period of use has expired. When the DST clientmodule 34 deletes the one or more primary slices 356, the DST clientmodule 34 determines whether to re-store the processed data 354. Thedetermining may be based on one or more of storage goal, a re-storerequest, and a predetermination. When the DST client module 34determines to restore the processed data 354, the DST client module 34retrieves at least one of the one more sets of secondary slices 358 andthe one or more sets of primary slices 356 and decodes retrieved slicesto reproduce one or more of the processed data 354 and the data 352.

The DST client module 34 encodes one or more of the processed data 354and the data 352 utilizing the dispersed storage error coding functionin accordance with modified dispersed storage error coding functionparameters to produce one or more sets of modified secondary slices. Theencoding includes determining the modified dispersed storage errorcoding function parameters. The determining includes one or more ofreceiving, retrieving, and generating based on the storage goal. Forexample, the DST client module 34 determines the modified dispersedstorage error coding function parameters to result in improvedreliability of storage of the one or more sets of modified secondaryslices when the storage goal indicates to re-store the processed data354 with improved reliability when the one or more primary slices 356have been deleted. The DST client module 34 stores the one or more setsof modified secondary slices in the DSTN module 22. The storing includesselecting a set of DST execution units 36 in accordance with a storagegoal for modified secondary slices.

FIG. 40B is a schematic block diagram of an embodiment of a dispersedstorage network system that includes a computing device 360, a dispersedstorage network (DSN) 362, and a plurality of sources 382. A source 382of the plurality of sources 382 may include a communication system, acomputing system, and a storage system. Each of the plurality of sources382 sources data 384. The DSN 362 includes a plurality of storagedevices 368 for storage of the data 384. A subset of the plurality ofstorage devices 368 may be organized into at least one temporary memory364 to facilitate storage of the data 384 on a temporary temporal basis.Another subset of the plurality of storage devices 368 may be organizedinto at least one non-temporary memory 366 to facilitate storage of thedata 384 on a non-temporary temporal basis. Each storage device 368 ofthe plurality of storage devices 368 may be implemented utilizing atleast one of a storage server, a storage unit, a storage module, amemory device, a memory, a distributed storage (DS) unit, a DSprocessing unit, a distributed storage and task (DST) execution unit, auser device, a DST processing unit, and a DST processing module.

The computing device 360 includes a dispersed storage (DS) module 370and a memory 372. The memory 372 may be implemented using one or more ofa solid state memory device, an optical disk memory device, and amagnetic disk memory device. The memory 372 may be organized to includea queue 374 for storage of the data 384. For example, an array of solidstate memory devices provides the queue 374 to store the data 384. Thecomputing device 360 may be implemented utilizing at least one of aserver, a storage unit, a distributed storage and task network (DSTN)managing unit, a DSN managing unit, a DS unit, the storage device 368, astorage server, a storage module, a DS processing unit, a DST executionunit, a user device, a DST processing unit, and a DST processing module.For example, computing device 360 is implemented as the DS processingunit. The DS module 370 includes a queue module 376, a temporary storagemodule 378, and a storage module 380.

The system functions to receive the data 384 from the plurality ofsources 382, temporarily store the data 384 in the DSN 362, andnon-temporarily store the data 384 in the DSN 362. With regards to thereceiving the data 384 from the plurality of sources 382, the queuemodule 376 receives the data 384 from the plurality of sources 382 andqueues the data 384 for storage in the DSN. For example, the queuemodule 376 stores the data 384 in the queue 374 of the memory 372.

With regards to the temporarily storing the data 384 in the DSN 362, thetemporary storage module 378 utilizes temporary dispersed storage errorencoding parameters 390 for efficient and reliable error encodedtemporary storage of the data in the DSN. The temporary storage module378 stores the data 384 in the DSN 362 by performing a series oftemporary storage steps. In a first temporary storage step, thetemporary storage module 378 determines a loading of the queue 374corresponding to the queuing of the data 384. In a second temporarystorage step, the temporary storage module 378 determines a desiredreliability-duration of the temporary storage based on the loading(e.g., minimal risk of losing data within a given period of time). Thedesired reliability-duration may be established such that it isextremely unlikely that enough storage failures will occur within thegiven period of time to cause loss of data, thus providing a same levelof reliability as using dispersed storage error encoding parametersassociated with a higher level of reliability, but for a short period oftime.

In a third temporary storage step, the temporary storage module 378determines the temporary dispersed storage error encoding parameters 390based on the loading and the desired reliability-duration. For example,the temporary storage module 378 determines the temporary dispersedstorage error encoding parameters 390 associated with lower than averageretrieval reliability when queue loading (e.g., utilization) is above ahigh threshold level. In a fourth temporary storage step, the temporarystorage module 378 retrieves a data object 386 (e.g., data block, datafile, streaming video portion, etc.) of the data 384 from the queue 374.In a fifth temporary storage step, the temporary storage module 378encodes the retrieved data object 386 of the data 384 in accordance withthe temporary dispersed storage error encoding parameters 390 to producefirst encoded data slices 388. In a sixth temporary storage step, thetemporary storage module 378 stores the first encoded data slices 388 inthe temporary memory 364 of the DSN 362. In a seventh temporary storagestep, the temporary storage module 378 may adjust the temporarydispersed storage error encoding parameters 390 based on variations ofthe loading of the queue with regards to storing another data object386.

With regards to the non-temporarily storing the data 384 in the DSN 362,the storage module 380, for the data object 386 of the data 384temporarily stored in the DSN 362 in accordance with the temporarydispersed storage error encoding parameters 390, performs a series ofstorage steps. In a first storage step, the storage module 380, withinthe time period corresponding to reliability of the temporary dispersedstorage error encoding parameters 390, determines non-temporary storageparameters for the data object. The storage module 380 may determine thetime period corresponding to the reliability of the temporary dispersedstorage error encoding parameters 390 based on a calculated risk of lossof data due to storage device failures involved when the data 384 isstored using the temporary dispersed storage error encoding parameters390.

The non-temporary storage parameters include at least one of a level ofcompression of the data object 386 prior to the error encoding of thedata object 386, non-temporary dispersed storage error encodingparameters 390 with an objective of one or more of: long term storagereliability, ease of access, and storage size within the DSN 362,indicating that the first encoded data slices 388 are to be stored asnon-temporary data slices (e.g., second encoded data slices 392), anddisposition of the first encoded data slices 388 (e.g., keep for anothergiven period of time, delete). The storage module 380 may determine thenon-temporary storage parameters by a series of ascertaining steps. Afirst ascertaining step includes the storage module 380 ascertainingsource information regarding a source of the data object 386. A secondascertaining step includes the storage module 380 ascertaining userinformation regarding a user of the data object 386. A thirdascertaining step includes the storage module 380 ascertaining contentinformation regarding content of the data object 386. A fourthascertaining step includes the storage module 380 determining thenon-temporary storage parameters based on at least one of the sourceinformation, the user information, and the content information.Alternatively, the storage module 380 establishes the non-temporarystorage parameters to correspond to the temporary dispersed storageerror encoding parameters 390 when reliability-duration of the temporarydispersed storage error encoding parameters 390 corresponds to a desiredreliability-duration of non-temporary storage of the data object 386.

In a second storage step, the storage module 380 retrieves the firstencoded data slices 388 regarding the data object 386 from the DSN 362,where the data object 386 was error encoded in accordance with thetemporary dispersed storage error encoding parameters 390 to produce thefirst encoded data slices 388. In a third storage step, the storagemodule 380 reconstructs the data object 386 from the first encoded dataslices 388 in accordance with the temporary dispersed storage errorencoding parameters 390. In a fourth storage step, the storage module380 encodes the reconstructed data object 386 in accordance with thenon-temporary storage parameters for the data object 386 to produce thesecond encoded data slices 392. In a fifth storage step, the storagemodule 380 stores the second encoded data slices 392 in the DSN 362. Forexample, the storage module 380 stores the second encoded data slices392 in the non-temporary memory 366 of the DSN 362. The storage module380 may delete the first encoded data slices 388 from the temporarymemory 364 when the second encoded data slices 392 are stored in thenon-temporary memory 366. The storage module 380 may queue processing(e.g., performing the series of storage steps) of the data objects 386temporarily stored in the DSN 362 in accordance with the temporarydispersed storage error encoding parameters 390 and adjust processingpriority of one or more of the data objects 386 based on elapsed timesince temporary storage and the time period.

FIG. 40C is a flowchart illustrating an example of non-temporarilystoring temporarily stored data. The method begins at step 400 where aprocessing module (e.g., a dispersed storage processing module of acomputing device) receives data from a plurality of sources. The methodcontinues at step 402 where the processing module queues the data forstorage in a dispersed storage network (DSN). For example, theprocessing module extracts one or more data objects from the data andstores each of the one or more data objects in a queue of a memoryassociated with the computing device. The method continues at step 404where the processing module determines a loading of a queuecorresponding to the queuing of the data to initiate a process totemporarily store the data in the DSN that includes the processingmodule utilizing temporary dispersed storage error encoding parametersfor efficient and reliable error encoded temporary storage of the datain the DSN. The determining may be based on one or more of monitoring aqueue size, initiating a query, initiating a test, receiving a response,and receiving an error message.

The method continues at step 406 where the processing module determinesa desired reliability-duration of the temporary storage based on theloading (e.g., minimal risk of losing data within a given period oftime). The method continues at step 408 where the processing moduledetermines the temporary dispersed storage error encoding parametersbased on the loading and the desired reliability-duration.Alternatively, or in addition to, the processing module adjusts thetemporary dispersed storage error encoding parameters based onvariations of the loading of the queue. For example, the processingmodule adjusts the temporary dispersed storage error encoding parametersto facilitate faster temporary storage when the loading of the queueindicates that a queue loading level is greater than a high loadinglevel threshold.

The method continues at step 410 where the processing module retrieves adata object of the one or more data objects from the queue. The methodcontinues at step 412 where the processing module encodes the retrieveddata object of the data in accordance with the temporary dispersedstorage error encoding parameters to produce first encoded data slices.The method continues at step 414 where the processing module stores thefirst encoded data slices in temporary memory of the DSN. The methodcontinues at step 416 where the processing module adjusts priority ofprocessing of temporarily stored data objects. The adjusting includesqueuing processing of the data objects temporarily stored in the DSN inaccordance with the temporary dispersed storage error encodingparameters and adjusting the processing priority of one or more of thedata objects based on elapsed time since temporary storage and a timeperiod corresponding to reliability of the temporary dispersed storageerror encoding parameters. The processing module may determine the timeperiod corresponding to the reliability of the temporary dispersedstorage error encoding parameters based on a calculated risk of loss ofdata due to storage device failures involved when the data is storedusing the temporary dispersed storage error encoding parameters.

For the data object of the data temporarily stored in the DSN inaccordance with the temporary dispersed storage error encodingparameters, within the time period corresponding to reliability of thetemporary dispersed storage error encoding parameters, the methodcontinues at step 418 where the processing module determinesnon-temporary storage parameters for the data object. The non-temporarystorage parameters include at least one of a level of compression of thedata object prior to the error encoding of the data object,non-temporary dispersed storage error encoding parameters with anobjective of one or more of: long term storage reliability, ease ofaccess, and storage size within the DSN, indicating that the firstencoded data slices are to be stored as non-temporary data slices (e.g.,the second encoded data slices), and disposition of the first encodeddata slices (e.g., keep for a given period of time, delete). Thedetermining non-temporary storage parameters for the data objectincludes a series of ascertaining steps. A first ascertaining stepincludes the processing module ascertaining source information regardinga source of the data object (e.g., an identifier associated with anentity that provided the data object). A second ascertaining stepincludes the processing module ascertaining user information (e.g., useridentifier) a regarding a user of the data object. A third ascertainingstep includes the processing module ascertaining content information(e.g., content type, content size, content storage priority, a contentretrieval reliability requirement) regarding content of the data object.A fourth ascertaining step includes the processing module determiningthe non-temporary storage parameters based on at least one of the sourceinformation, the user information, and the content information.Alternatively, the processing module establishes the non-temporarystorage parameters to correspond to the temporary dispersed storageerror encoding parameters when reliability-duration of the temporarydispersed storage error encoding parameters corresponds to a desiredreliability-duration of non-temporary storage of the data object.

The method continues at step 420 where the processing module retrievesthe first encoded data slices regarding the data object from the DSN,where the data object was error encoded in accordance with the temporarydispersed storage error encoding parameters to produce the first encodeddata slices. The method continues at step 422 where the processingmodule reconstructs the data object from the first encoded data slicesin accordance with the temporary dispersed storage error encodingparameters. For example, the processing module decodes the first encodeddata slices using a dispersed storage error coding function inaccordance with the temporary dispersed storage error encodingparameters to produce a reconstructed data object. The method continuesat step 424 where the processing module encodes the reconstructed dataobject in accordance with the non-temporary storage parameters for thedata object to produce second encoded data slices. The method continuesat step 426 where the processing module stores the second encoded dataslices in the DSN. The storing includes the processing module sendingthe second encoded data slices to a non-temporary memory of the DSN. Themethod continues at step 428 where the processing module deletes thefirst encoded data slices from the temporary memory (e.g., when thesecond encoded data slices are stored in the non-temporary memory).

FIG. 41A is a schematic block diagram of another embodiment of adistributed computing system that includes a user device 14, a loadbalance module 430, a plurality of distributed storage and task (DST)processing units 16 (DS processing units), and a distributed storage andtask network (DSTN) module 22 (DSN module). The DSTN module 22 includesa plurality of DST execution units 36 (storage units). The load balancemodule 430 may be implemented as at least one of a computer, a computingdevice, a server, a user device 14, or a DST processing unit 16. Theload balance module 430 receives data 432 for storage in the DSTN module22 from the user device 14. The load balance module 430 determines whichone of the plurality of DST processing units 16 to facilitate storage ofthe data 432 as encoded data slices (slices) 434 in the DSTN module 22in accordance with a load balanced approach. The load balanced approachmay be accomplished in a variety of ways. In a first way, a DSTN addressassociated with the data 432 is utilized to identify one or moreassociated DST processing units 16 as a first selection step and the oneof the one or more DST processing units 16 is further identified as afinal selection step. The identifying the one or more associated DSTprocessing units 16 may be based on accessing an affinity table thatassociates the DSTN address with the one or more DST processing units16. The further identifying the one DST processing unit 16 may be basedon one or more of DST processing unit availability, DST processing unitpriority, or DST processing unit attributes. In a second way, a DSTprocessing unit 16 is selected randomly. In a third way, a DSTprocessing unit 16 is selected based on a requesting entity ID and anassociation between requesting entity ID and the plurality of DSTprocessing units 16.

In an example of operation, the load balance module 430 receives a dataaccess request that includes the data 432 from user device 14 anddetermines a DSTN address associated with the data 432 of the dataaccess request. The determining may be based on one or more of a lookup,receiving, or generating a source name based on a data identifier (ID)of the data 432 or a requesting entity ID associated with the userdevice 14. The load balance module 430 accesses the affinity tableutilizing the DSTN address to identify the one or more DST processingunits 16 associated with a range of DSTN addresses that includes theDSTN address. The load balance module 430 selects one of the one or moreDST processing units 16 based on a priority level associated with theone or more DST processing units 16. For example, the load balancemodule selects the one DST processing unit 16 when the one DSTprocessing unit 16 is associated with a priority level of 1 when otherDST processing units 16 of the one or more DST processing units 16 areassociated with priority level numbers of lower priority. The loadbalance module 430 forwards the data access request to the one selectedDST processing unit 16. The one selected DST processing unit 16 receivesthe data access request and processes the access request with regards toslices 434 stored in the DSTN module 22.

FIG. 41B is a diagram illustrating an example of an affinity table 436that includes one or more table entries corresponding to one or moredistributed storage and task (DST) processing units. Each table entry ofthe one more table entries includes a distributed storage and tasknetwork (DSTN) address range entry of a DSTN address range field 438, apriority entry of a priority field 440, a DST processing unit identifier(ID) entry of a DST processing unit ID field 442, or DST processing unitattributes of a DST processing unit attributes field 444. The DSTNaddress range entry includes a range of DSTN addresses associated with aDST processing unit of the table entry. The priority entry indicates apriority level associated with the DST processing unit of the entryrelative to other DST processing units associated with substantially thesame DSTN address range. The DST processing unit ID entry includes anidentifier associated with the DST processing unit of the table entry.The DST processing unit attributes include one or more attributesassociated with the DST processing unit of the table entry. Theattributes include one or more of a physical location identifier, ahistoric reliability performance level, a historic availabilityperformance level, a loading capacity level, a bandwidth capabilitylevel, a processing level indicator, or an historic error rate level. Amethod of operation of a system to utilize the affinity table isdiscussed in greater detail with reference to FIG. 41C.

FIG. 41C is a flowchart illustrating an example of load-balancing. Themethod begins at step 446 where a processing module (e.g., of a loadbalance module) receives a distributed storage and task network (DSTN)access request. The request may include one or more of a data identifier(ID), data, a DSTN address associated with the data, a request type, ora requesting entity ID. The method continues at step 448 where theprocessing module identifies a DSTN address of the DSTN access request.The identifying may be based on one or more of extracting the DSTNaddress from the access request, a lookup (e.g., for a read requesttype), or generating the DSTN address (e.g., for a write request type)based on one or more of the requesting entity ID, the data, a vault IDassociated with the requesting entity, a vault lookup, or the data ID.

The method continues at step 450 where the processing module identifiesone or more DST processing units affiliated with the DSTN address. Theidentifying may be based on one or more of receiving, an affinity tablelookup, or a query. For example, the processing module accesses anaffinity table based on the DSTN address to identify the one or more DSTprocessing units associated with a DSTN address range that includes theDSTN address. For each of the one or more DST processing units, themethod continues at step 452 where the processing module determines anavailability level. The determining may be based on one or more ofreceiving an error message, accessing an availability list, sending aquery, or performing a test.

For each of the one or more DST processing units, the method continuesat step 454 where the processing module determines a priority level. Thedetermining may be based on one or more of an affinity table lookup,receiving a list, or sending a query. The method continues at step 456where the processing module selects one DST processing unit of the oneor more DST processing units based on one or more of the availabilitylevel and the priority level. For example, the processing module selectsan available DST processing unit associated with a greater prioritylevel then other available DST processing units. The method continues atstep 458 where the processing module forwards the DSTN access request tothe selected DST processing unit. Alternatively, or in addition to, theprocessing module appends the DSTN address to the access request whenthe request type is a write request and the processing module hasgenerated the DSTN address as a new DSTN address associated with thedata.

FIG. 42 is a flowchart illustrating another example of load-balancing,which include similar steps to FIG. 41C. The method begins with steps446-450 of FIG. 41C where a processing module (e.g., of a load balancemodule of a computing device) receives a distributed storage and tasknetwork (DSTN) access request, identifies a DSTN address of the DSTNaccess request, and identifies one or more DST processing unitsaffiliated with the DSTN address. The method continues at step 460 wherethe processing module selects one of the one or more DST processingunits based on DST processing unit attributes. The selecting includesobtaining DST processing unit attributes associated with the one or moreDST processing units affiliated with the DSTN address. The obtaining maybe based on one or more of receiving, a query, or an affinity tablelookup. The selecting includes optimizing a match of DST processing unitattributes to the access request. For example, the processing moduleselects a DST processing unit associated with a superior processinglevel indicator and an available storage capacity indicator thatcompares favorably to the access request and to similar metrics of otherDST processing units associated with the DSTN address.

The method continues at step 462 where the processing module determineswhether the selected DST processing unit is associated with a favorableavailability level. The determining may be based on one or more ofsending a query, receiving an availability indicator, a DST processingunit status table lookup, or comparing an availability level to anavailability level threshold. The processing module determines that theselected DST processing unit is associated with a favorable availabilitylevel when the availability level is greater than the availability levelthreshold. The method branches to step 458 of FIG. 41C when theprocessing of determines that the selected DST processing unit isassociated with the favorable availability level. The method continuesto step 464 when the processing of determines that the selected DSTprocessing unit is not associated with the favorable availability level.The method continues at step 464 where the processing moduledeterministically selects another of the one or more DST processingunits affiliated with the DSTN address. For example, the processingmodule picks an nth DST processing unit affiliated with the DSTN address(e.g., in a circular fashion when n is greater than a number of DSTprocessing units affiliated with the DSTN address). The method loopsback to step 462. The method continues at step 458 of FIG. 41C where theprocessing module forwards the DSTN access request to the selected DSTprocessing unit when the processing module determines that the selectedDST processing unit is associated with the favorable availability level.

FIG. 43A is a schematic diagram illustrating another example ofload-balancing within a storage network (e.g., DSN/DSTN), which includesone or more similar steps to FIG. 41C. Load balancing module 430 (of acomputing device) receives a storage network access request (e.g., R/Wof encoded data slices). The load balancing module (processing module)determines whether the storage network access request is write-centricor read-centric based on a request type of the request. Thewrite-centric access request includes at least one of a write slicerequest and a delete slice request. The read-centric access requestincludes at least one of a read slice request, a list request, and alist digest request. When the load balancing module determines that thestorage network access request is read-centric, one or more read-centricprocessing units are identified (shown as 16-1 and 16-2). A read-centricprocessing unit may be associated with a lower processing capability(e.g., look-up a location of requested data in a storage network accessrequest and retrieve from the location)) than a write-centric processingunit (shown as 16-N−1 thru 16-N) where storage of encoded data slicescan require additional processing (e.g., free space analysis, addressspace assignment, mapping, movement of encoded data slices over time,storage error processing, device profile and performance determinations,or device failures, etc.), one or more these processing steps may not beneeded by a simple read. The identifying may be based on one or more ofa lookup, a predetermination, a query, and a test. Load balancing module430 determines whether attributes of one of the one or more read-centricprocessing units meets the minimum requirement(s) level of the storagenetwork access request. The attributes include one or more of anavailable processing capacity level, available processing capability, amemory availability level, and an available input/output bandwidthlevel. The determining may be based on comparing an estimated minimumrequirement level of the access request to the attributes of the oneprocessing unit 16-1. For example, the load balancing module 430determines that the minimum requirement level is not one of theattributes of the one processing unit 16-1 that compare favorably to theestimated minimum requirement level and therefore determines that theone DST processing unit 16-1 does not meet the minimum requirementlevel. Other profile, performance or historical comparison criteria maybe substituted when determining the estimated minimum requirement(s)level.

FIG. 43B is a system diagram illustrating the load balancing moduleidentifying one or more write-centric processing units (shown as 16-N−1thru 16-N). The identifying may be based on one or more of a lookup, apredetermination, a query, and a test. The load balancing module selectsone processing unit (16-N) of the one or more write-centric processingunits. The selecting may be based on one or more of identifying the oneprocessing unit (16-N) associated with a most favorable level of one ormore of availability, memory capacity, capability, and reliability. Theload balancing module forwards the storage network access request to theselected processing unit.

FIG. 43C is a flowchart illustrating another example of load-balancing,which include similar steps to FIG. 41C. The method begins with step 446of FIG. 41C where a processing module (e.g., of a load balance module ofa computing device) receives a storage network (e.g., DSN/DSTN) accessrequest. The method continues at step 466 where the processing moduledetermines whether the storage network access request is write-centricor read-centric based on a request type of the request. Thewrite-centric storage network access request includes at least one of awrite slice request and a delete slice request. The read-centric storagenetwork access request includes at least one of a read slice request, alist request, and a list digest request. The method branches to step 472when the processing module determines that the storage network accessrequest is write-centric. The method continues to step 468 when theprocessing module determines that the storage network access request isread-centric.

The method continues at step 468 where the processing module identifiesone or more read-centric storage network processing units. Aread-centric storage network processing unit may be associated with alower processing capability than a write-centric storage networkprocessing unit. The identifying may be based on one or more of alookup, a predetermination, a query, and a test. The method continues atstep 470 where the processing module determines whether attributes ofone of the one or more read-centric storage network processing unitsmeets the minimum requirement level of the access request. Theattributes includes one or more of an available processing capacitylevel, available processing capability, a memory availability level, andan available input/output bandwidth level. The determining may be basedon comparing an estimated minimum requirement level of the accessrequest to the attributes of the one storage network processing unit.For example, the processing module determines that the minimumrequirement level is not one of the attributes of the one storagenetwork processing unit that compare favorably to the estimated minimumrequirement level. The method branches to step 458 of FIG. 41C when theprocessing module determines that the one storage network processingunit meets the minimum requirement level. The method continues to step472 when the processing module determines that the one storage networkprocessing unit does not meet the minimum requirement level.

The method continues at step 472 where the processing module identifiesone or more write-centric storage network processing units. Theidentifying may be based on one or more of a lookup, a predetermination,a query, and a test. The method continues at step 474 where theprocessing module selects one storage network processing unit of the oneor more write-centric storage network processing units. The selectingmay be based on one or more of identifying the one storage networkprocessing unit associated with a most favorable level of one or more ofavailability, memory capacity, capability, and reliability. The methodcontinues with step 458 of FIG. 41C where the processing module forwardsthe storage network access request to the selected storage networkprocessing unit.

FIG. 44A is a schematic block diagram of another embodiment of adistributed storage and task (DST) client module 34 that includes adeterministic source name generator 476 and a function selector 478. Thedeterministic source name generator 476 receives a write request 480 andgenerates a source name (or identifier) 484 associated with the writerequest 480 based on a function 482 from the function selector 478. Thesource name 484 is associated with at least one distributed storage andtask network (DSTN) address range of a plurality of DSTN address ranges,where each DSTN address range utilizes a specific set of storageresources within a DSTN module. Each set of storage resources may beassociated with a storage attribute. The storage attribute includes oneor more of high reliability, low reliability, low cost, high cost, localstorage, remote storage, etc. For example, storage resources associatedwith a first DSTN address range may be associated with above averagestorage reliability and storage resources associated with a second DSTNaddress range may be associated with low-cost and average storagereliability.

The function selector 478 receives the write request 480 and selects thefunction 482 based on one or more of a data identifier (ID) of the writerequest, a requesting entity ID, an available storage level, a securitygoal, a lookup, and a reliability goal. For example, the functionselector 478 selects a function associated with high storage reliabilitywhen the requesting entity ID is associated with a high storagereliability profile. The function selector 478 outputs the function 482to the deterministic source name generator 476 for utilization ingenerating the source name 484 based on the write request 480. Forexample, the deterministic source name generator 476 performs a hashingfunction on the data ID of the write request 480 to produce an interimresult, truncates the interim result to produce a truncated interimresult, and adds the truncated interim result to a starting DSTN addressof a DSTN address range associated with a desired set of storageresources within the DSTN module to produce the source name 484.

FIG. 44B is a schematic block diagram of another embodiment of adispersed storage network system that includes a computing device 490, adispersed storage network (DSN) 492, and a plurality of authorized users516. An authorized user 516 of the plurality of authorized users 516includes a user device. Each of the plurality of authorized users 516provides data objects 508 for storage in the DSN 492. The DSN 492includes a multitude of storage nodes 496 for storage of the dataobjects 508 to provide an on-line media storage system. The multitude ofstorage nodes 496 may be organized into one or more storage node sets494. Each storage node 496 of the plurality of storage nodes 496 may beimplemented utilizing at least one of a storage server, a storage unit,a storage module, a memory device, a memory, a distributed storage (DS)unit, a DS processing unit, a distributed storage and task (DST)execution unit, a user device, a DST processing unit, and a DSTprocessing module.

The computing device 490 includes a dispersed storage (DS) module 498and a memory 500. The memory 500 may be implemented using one or more ofa solid state memory device, an optical disk memory device, and amagnetic disk memory device. The memory 500 may be organized to includea buffer 502 for storage of the data objects 508. For example, anarray/first plurality of solid state memory devices of a first memorytype provides the buffer 502 to temporarily store the data objects 508.The computing device 490 may be implemented utilizing at least one of aserver, a storage unit, a distributed storage and task network (DSTN)managing unit, a DSN managing unit, a DS unit, the storage node 496, astorage server, a storage module, a DS processing unit, a DST executionunit, a user device, a DST processing unit, and a DST processing module.For example, computing device 490 is implemented as the DS processingunit. The DS module 498 includes a receive module 503, a select storagemodule 504, and a store module 506.

The system functions to receive the data objects 508 from the pluralityof authorized users 516, select a set of storage nodes 494 for storageof a data object 508 of the data objects 508, and store the data object508 in the selected set of storage nodes 494. With regards to thereceiving the data objects 508 from the plurality of authorized users516, the receive module 503 randomly and continuously receives, forstorage in the on-line media storage system (e.g., the DSN 492), thedata objects 508 from the plurality of authorized users 516, where adata type (e.g., text, music, voice, image, video) of the data object508 of the data objects 508 is one of a plurality of different datatypes and where memory space within the on-line media storage system isprimarily allocated to the plurality of authorized users 516 on anas-needed basis. As such, an authorized user 516 of the plurality ofauthorized users 516 has no minimal pre-allocated memory space withinthe on-line media storage system. The receive module 503, prior toprocessing the data objects 508, temporarily stores the data objects 508in the buffer 502 and retrieves the data objects 508 from the buffer 502in accordance with a priority protocol to process the data objects 508.The priority protocol includes at least one of a first in first outapproach, a parallel processing approach, a last in first out approach,and a random approach. The receive module 503 may output the dataobjects 508 retrieved from the buffer 502 to the select storage module504 for the processing of the data objects 508. Alternatively, thereceive module 503 immediately forwards the data objects 508 receivedfrom the plurality of authorized users 516 directly to the selectstorage module 504.

With regards to the selecting the set of storage nodes 494 for storageof the data object 508, the select storage module 504 processes the dataobjects 508 for storage by entering a loop that includes a series ofloop steps. In a first loop step, for the data object 508 from theauthorized user 516 of the plurality of authorized users 516, the selectstorage module 504 determines a system level storage efficiencypreference based on system storage node information and one or more of:the data type of the data object 508, data size of the data object 508,identity of the authorized user 516, location of the authorized user516, system privileges of the authorized user 516, storage preferencesof the authorized user 516, and user group affiliation of the authorizeduser 516. The system storage node information includes one or more ofmemory utilization of each of the multitude of storage nodes 496,available memory of each of the multitude of storage nodes 496,allocated data type storage preference of each of the multitude ofstorage nodes 496 (e.g., a storage node is designated as primarilystoring video, but can store other data types), access history of eachof the multitude of storage nodes 496 (e.g., access speeds, on-linepercentage, failure rates), geographic location of each of the multitudeof storage nodes 496, and physical characteristics of each of themultitude of storage nodes 496 (e.g., hardware configuration, softwareconfiguration, network connections, Internet protocol address, memorycapacity level, memory utilization level).

The select storage module 504 determines the system level storageefficiency preferences by a series of preference steps. A firstpreference step includes the select storage module 504 interpreting oneor more data attributes associated with a data object, such as: the datatype of the data object 508, the data size of the data object 508, theidentity of the authorized user 516, the location of the authorized user516, the system privileges of the authorized user 516, the storagepreferences of the authorized user 516, and the user group affiliationof the authorized user 516 to produce a user storage preference. Asecond preference step includes the select storage module 504identifying a preliminary set of storage nodes 494 (e.g., of manycandidate storage node sets 494) based on the user storage preference. Athird preference step includes the select storage module 504 determiningpreliminary dispersed storage error encoding parameters based on theuser storage preference. A fourth preference step includes the selectstorage module 504 determining the system storage node information forthe preliminary set of storage nodes. When the system storage nodeinformation indicates that the preliminary set of storage nodes isappropriate for storing encoded data slices, a fifth preference stepincludes the select storage module 504 utilizing the preliminary set ofstorage nodes for the set of storage nodes 494 and utilizing thepreliminary dispersed storage error encoding parameters as dispersedstorage error encoding parameters 514. When the system storage nodeinformation indicates that the preliminary set of storage nodes is notappropriate for storing the encoded data slices, a sixth preference stepincludes the select storage module 504 selecting the set of storagenodes 494 based on a compromise between the user storage preference andthe system storage node information of the set of storage nodes.

In a second loop step, the select storage module 504 selects the set ofstorage nodes 494 from a multitude of storage nodes 496 of the on-linemedia storage system based on the system level storage efficiencypreference (e.g., best match) to provide a storage node selection 512.In a third loop step, the select storage module 504 determines thedispersed storage error encoding parameters based on the set of storagenodes (e.g., a mapping lookup) or based on the system level storageefficiency preference. In an example of operation of the select storagemodule 504, the select storage module 504 determines the data type ofthe data object 508 to be a video file and determines geographiclocation of the authorized user. Next, the select storage module 504prioritizes allocated data type storage preference and geographiclocation of the system level storage efficiency preference and selectsthe set of storage nodes primarily based on the data object being thevideo file and the geographic location of the authorized user. Next, theselect storage module 504 determines the dispersed storage errorencoding parameters 514 primarily based on the data object being thevideo file.

With regards to the storing of the data object 508 in the selected setof storage nodes 494, the store module 506 processes the data objects508 for storage by continuing the loop that includes further loop stepsof the series of loop steps. In a fourth loop step, the store module 506encodes the data object 508 in accordance with the dispersed storageerror encoding parameters 514 to produce the encoded data slices. In afifth loop step, the store module 506 generates system addressinginformation for the encoded data slices based on the encoded dataslices, the set of storage nodes 494, and identity of the data object508.

The store module 506 generates the system addressing information for theencoded data slices by generating, as the system addressing information,slice names for the encoded data slices, where a slice name of the slicenames includes identity of one of the encoded data slices, an address ofa range of addresses assigned to a storage node 494 of the set ofstorage nodes 494, and identity of the data object 508. For example, thestore module 506 selects a deterministic function based on one or moreof the data object 508, the data type of the data object 508, the datasize of the data object 508, the identity of the authorized user 516,the location of the authorized user 516, the system privileges of theauthorized user 516, the storage preferences of the authorized user 516,the user group affiliation of the authorized user 516, the data objectsize indicator, a data object type indicator, the identifier (ID) of thedata object, a vault identifier associated with the authorized user 516,a registry lookup, a vault lookup, a DSN status indicator, and a storagerequirement. The deterministic function includes one or more of a finitefield arithmetic function, a hashing function, a cyclic redundancy codefunction, a hash based message authentication code function, a maskgenerating function, and a sponge function. Next, the store module 506transforms the ID of the data object using the deterministic function toproduce a transformed data object ID. The transforming includes applyingthe deterministic function to the ID of the data object to produce thetransformed data object ID. Next, the store module 506 generates thesystem addressing information (e.g., address) using the transformed dataobject ID, where the address falls within the range of addressesassigned to the storage node of the set of storage nodes. For instance,the generating includes using the transformed data object ID as anoffset from a starting address of the range of addresses.

In a sixth loop step, the store module 506 updates a user profile (e.g.,a directory entry, a dispersed hierarchical index entry) for theauthorized user 516 to include the system addressing information for thedata object 508. In a seventh loop step, the store module 506 issueswrite commands 510 to the set of storage nodes 494 for storing theencoded data slices in the set of storage nodes 494 (e.g., in a secondplurality of memory devices of a second memory type that differs fromthe first plurality of memory devices of the buffer 502). The issuingincludes generating the write commands 510 to include the encoded dataslices and the slice names for the encoded data slices. The issuingfurther includes sending the write commands 510 to the set of storagenodes 494. Next, the loop is repeated by the select storage module 504and the store module 506 for another data object 508 from anotherauthorized user 516 of the plurality of authorized users 516 as the dataobject 508 from the authorized user 516.

FIG. 44C is a flowchart illustrating an example of storing. The methodbegins at step 520 where a processing module (e.g., of a dispersedstorage (DS) processing module of one or more computing devicesassociated with an on-line media storage system) randomly andcontinuously receives, for storage in the on-line media storage system,data objects from a plurality of authorized users. A data type of a dataobject of the data objects is one of a plurality of different data typesand memory space within the on-line media storage system that isprimarily allocated to the plurality of authorized users on an as-neededbasis. The method continues at step 522 where the processing module,prior to processing the data objects, temporary stores the data objectsin a buffer (e.g., a first plurality of memory devices, of a firstmemory type, associated with the processing module). The methodcontinues at step 524 where the processing module retrieves the dataobjects from the buffer in accordance with a priority protocol toprocess the data objects. For a data object from an authorized user ofthe plurality of authorized users, the method continues at step 526where the processing module determines a system level storage efficiencypreference based on system storage node information and one or more of:the data type of the data object, data size of the data object, identityof the authorized user, location of the authorized user, systemprivileges of the authorized user, storage preferences of the authorizeduser, and user group affiliation of the authorized user.

The determining the system level storage efficiency preference includesa series of preference determining steps. A first preference determiningstep includes interpreting one or more of: the data type of the dataobject, the data size of the data object, the identity of the authorizeduser, the location of the authorized user, the system privileges of theauthorized user, the storage preferences of the authorized user, and theuser group affiliation of the authorized user to produce a user storagepreference. A second preference determining step includes identifying apreliminary set of storage nodes based on the user storage preference. Athird preference determining step includes determining preliminarydispersed storage error encoding parameters based on the user storagepreference. A fourth preference determining step includes determiningthe system storage node information for the preliminary set of storagenodes. When the system storage node information indicates that thepreliminary set of storage nodes is appropriate for storing the encodeddata slices, a fifth preference determining step includes utilizing thepreliminary set of storage nodes for the set of storage nodes andutilizing the preliminary dispersed storage error encoding parameters asthe dispersed storage error encoding parameters. When the system storagenode information indicates that the preliminary set of storage nodes isnot appropriate for storing the encoded data slices, a six preferencedetermining step includes selecting the set of storage nodes based on acompromise between the user storage preference and the system storagenode information of the set of storage nodes.

The method continues at step 528 where the processing module selects aset of storage nodes from a multitude of storage nodes of the on-linemedia storage system based on the system level storage efficiencypreference. Alternatively, the processing module identifies two or morecandidate sets of storage nodes from a multitude of storage nodes basedon the system level storage efficiency preference, where each of the twoor more sets of candidate storage nodes compare favorably to the systemlevel storage efficiency preference. The method continues at step 530where the processing module determines dispersed storage error encodingparameters based on the set of storage nodes or based on the systemlevel storage efficiency preference. For example, the processing moduledetermines the dispersed storage error encoding parameters to include apillar width of 16 when the set of storage nodes includes 16 storagenodes. As another example, the processing module determines thedispersed storage error encoding parameters to include a complementarypairing of the pillar width and a decode threshold to provide a level ofretrieval reliability that compares favorably to the system levelstorage efficiency preference. The method continues at step 532 wherethe processing module encodes the data object in accordance with thedispersed storage error encoding parameters to produce encoded dataslices. For example, the processing module encodes the data object usinga dispersed storage error coding function in accordance with thedispersed storage error encoding parameters to produce the encoded dataslices that includes a plurality of sets of encoded data slices.

The method continues at step 534 where the processing module generatessystem addressing information for the encoded data slices based on theencoded data slices, the set of storage nodes, and identity of the dataobject. The generating the system addressing information for the encodeddata slices further includes generating, as the system addressinginformation, slice names for the encoded data slices, wherein a slicename of the slice names includes identity of one of the encoded dataslices, an address of a range of addresses assigned to a storage node ofthe set of storage nodes, and identity of the data object.Alternatively, the processing module selects one set of storage nodes ofthe two or more sets of candidate storage nodes when the two or moresets of candidate storage nodes have been selected, where the selectingof the one set of storage nodes is based on the generating of theaddressing information. For example, the processing module generates theslice names for the encoded data slices utilizing a deterministicfunction to select the address of the range of addresses assigned tostorage nodes of the two or more candidate sets of storage nodes. Forinstance, the processing module performs a deterministic function on theidentity of the data object to produce an offset and adds the offset toa starting address of the range of addresses to produce the address.

The method continues at step 536 where the processing module updates auser profile for the authorized user to include the system addressinginformation for the data object. For example, the processing moduleupdates an entry of at least one of a directory and a dispersedhierarchical index to associate the identity of the data object and thesystem addressing information, where the entry is associated with theauthorized user. The method loops back to step 526 for another dataobject from another authorized user of the plurality of authorized usersas the data object from the authorized user.

In an example of operation, the processing module determines the datatype of the data object to be a video file. Next, the processing moduledetermines geographic location of the authorized user and prioritizesallocated data type storage preference and geographic location of thesystem level storage efficiency preference. Next, the processing moduleselects the set of storage nodes primarily based on the data objectbeing the video file and the geographic location of the authorized userand determines the dispersed storage error encoding parameters primarilybased on the data object being the video file.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, text, graphics, audio, etc. any of which may generally bereferred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. For some industries, anindustry-accepted tolerance is less than one percent and, for otherindustries, the industry-accepted tolerance is 10 percent or more. Otherexamples of industry-accepted tolerance range from less than one percentto fifty percent. Industry-accepted tolerances correspond to, but arenot limited to, component values, integrated circuit process variations,temperature variations, rise and fall times, thermal noise, dimensions,signaling errors, dropped packets, temperatures, pressures, materialcompositions, and/or performance metrics. Within an industry, tolerancevariances of accepted tolerances may be more or less than a percentagelevel (e.g., dimension tolerance of less than +/−1%). Some relativitybetween items may range from a difference of less than a percentagelevel to a few percent. Other relativity between items may range from adifference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operablycoupled to”, “coupled to”, and/or “coupling” includes direct couplingbetween items and/or indirect coupling between items via an interveningitem (e.g., an item includes, but is not limited to, a component, anelement, a circuit, and/or a module) where, for an example of indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operableto”, “coupled to”, or “operably coupled to” indicates that an itemincludes one or more of power connections, input(s), output(s), etc., toperform, when activated, one or more its corresponding functions and mayfurther include inferred coupling to one or more other items. As maystill further be used herein, the term “associated with”, includesdirect and/or indirect coupling of separate items and/or one item beingembedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., indicates anadvantageous relationship that would be evident to one skilled in theart in light of the present disclosure, and based, for example, on thenature of the signals/items that are being compared. As may be usedherein, the term “compares unfavorably”, indicates that a comparisonbetween two or more items, signals, etc., fails to provide such anadvantageous relationship and/or that provides a disadvantageousrelationship. Such an item/signal can correspond to one or more numericvalues, one or more measurements, one or more counts and/or proportions,one or more types of data, and/or other information with attributes thatcan be compared to a threshold, to each other and/or to attributes ofother information to determine whether a favorable or unfavorablecomparison exists. Examples of such an advantageous relationship caninclude: one item/signal being greater than (or greater than or equalto) a threshold value, one item/signal being less than (or less than orequal to) a threshold value, one item/signal being greater than (orgreater than or equal to) another item/signal, one item/signal beingless than (or less than or equal to) another item/signal, oneitem/signal matching another item/signal, one item/signal substantiallymatching another item/signal within a predefined or industry acceptedtolerance such as 1%, 5%, 10% or some other margin, etc. Furthermore,one skilled in the art will recognize that such a comparison between twoitems/signals can be performed in different ways. For example, when theadvantageous relationship is that signal 1 has a greater magnitude thansignal 2, a favorable comparison may be achieved when the magnitude ofsignal 1 is greater than that of signal 2 or when the magnitude ofsignal 2 is less than that of signal 1. Similarly, one skilled in theart will recognize that the comparison of the inverse or opposite ofitems/signals and/or other forms of mathematical or logical equivalencecan likewise be used in an equivalent fashion. For example, thecomparison to determine if a signal X>5 is equivalent to determining if−X<−5, and the comparison to determine if signal A matches signal B canlikewise be performed by determining −A matches −B or not(A) matchesnot(B). As may be discussed herein, the determination that a particularrelationship is present (either favorable or unfavorable) can beutilized to automatically trigger a particular action. Unless expresslystated to the contrary, the absence of that particular condition may beassumed to imply that the particular action will not automatically betriggered. In other examples, the determination that a particularrelationship is present (either favorable or unfavorable) can beutilized as a basis or consideration to determine whether to perform oneor more actions. Note that such a basis or consideration can beconsidered alone or in combination with one or more other bases orconsiderations to determine whether to perform the one or more actions.In one example where multiple bases or considerations are used todetermine whether to perform one or more actions, the respective basesor considerations are given equal weight in such determination. Inanother example where multiple bases or considerations are used todetermine whether to perform one or more actions, the respective basesor considerations are given unequal weight in such determination.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing circuitry”, and/or “processing unit”may be a single processing device or a plurality of processing devices.Such a processing device may be a microprocessor, micro-controller,digital signal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, processing circuitry, and/or processing unitmay be, or further include, memory and/or an integrated memory element,which may be a single memory device, a plurality of memory devices,and/or embedded circuitry of another processing module, module,processing circuit, processing circuitry, and/or processing unit. Such amemory device may be a read-only memory, random access memory, volatilememory, non-volatile memory, static memory, dynamic memory, flashmemory, cache memory, and/or any device that stores digital information.Note that if the processing module, module, processing circuit,processing circuitry, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,processing circuitry and/or processing unit implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, processing circuitry and/or processing unitexecutes, hard coded and/or operational instructions corresponding to atleast some of the steps and/or functions illustrated in one or more ofthe Figures. Such a memory device or memory element can be included inan article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with one or more other routines. In addition, a flow diagrammay include an “end” and/or “continue” indication. The “end” and/or“continue” indications reflect that the steps presented can end asdescribed and shown or optionally be incorporated in or otherwise usedin conjunction with one or more other routines. In this context, “start”indicates the beginning of the first step presented and may be precededby other activities not specifically shown. Further, the “continue”indication reflects that the steps presented may be performed multipletimes and/or may be succeeded by other activities not specificallyshown. Further, while a flow diagram indicates a particular ordering ofsteps, other orderings are likewise possible provided that theprinciples of causality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, a quantum register or otherquantum memory and/or any other device that stores data in anon-transitory manner. Furthermore, the memory device may be in a formof a solid-state memory, a hard drive memory or other disk storage,cloud memory, thumb drive, server memory, computing device memory,and/or other non-transitory medium for storing data. The storage of dataincludes temporary storage (i.e., data is lost when power is removedfrom the memory element) and/or persistent storage (i.e., data isretained when power is removed from the memory element). As used herein,a transitory medium shall mean one or more of: (a) a wired or wirelessmedium for the transportation of data as a signal from one computingdevice to another computing device for temporary storage or persistentstorage; (b) a wired or wireless medium for the transportation of dataas a signal within a computing device from one element of the computingdevice to another element of the computing device for temporary storageor persistent storage; (c) a wired or wireless medium for thetransportation of data as a signal from one computing device to anothercomputing device for processing the data by the other computing device;and (d) a wired or wireless medium for the transportation of data as asignal within a computing device from one element of the computingdevice to another element of the computing device for processing thedata by the other element of the computing device. As may be usedherein, a non-transitory computer readable memory is substantiallyequivalent to a computer readable memory. A non-transitory computerreadable memory can also be referred to as a non-transitory computerreadable storage medium.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A computing device comprising: one or morenetwork interfaces; memory including operational instructions; and aprocessing module operably coupled to the memory and the one or morenetwork interfaces, the processing module configured to execute theoperational instructions to: receive a data object for storage in memoryof a storage network; forward, via the one or more network interfaces,the data object for storage in a first plurality of memory devices ofthe storage network, the first plurality of memory devices comprised ofa first memory type; determine a system level storage efficiencypreference for the data object based, at least in part, on a dataattribute associated with the data object; select, based at least inpart on the system level storage efficiency preference, a secondplurality of memory devices of the storage network, the second pluralityof memory devices comprised of a second memory type; determine errorencoding parameters based, at least in part, on the second plurality ofmemory devices; retrieve the data object from the first plurality ofmemory devices; encode the data object in accordance with the errorencoding parameters to generate a plurality of encoded data slices; andforward the plurality of encoded data slices to the second plurality ofmemory devices for storage therein.
 2. The computing device of claim 1,wherein the data attribute is a data size of the data object.
 3. Thecomputing device of claim 1, wherein the data attribute is a data typeof the data object.
 4. The computing device of claim 1, whereindetermining the system level storage efficiency preference for the dataobject is further based on system storage node information.
 5. Thecomputing device of claim 1, wherein determining the error encodingparameters is further based on the system level storage efficiencypreference.
 6. The computing device of claim 1, wherein the firstplurality of memory devices is included in a first set of storage nodesof the storage network and the second plurality of memory devices isincluded in a second set of storage nodes of the storage network.
 7. Thecomputing device of claim 6, wherein determining the error encodingparameters includes determining the error encoding parameters based on anumber of storage nodes of the second set of storage nodes.
 8. Thecomputing device of claim 1, wherein the processing module is furtherconfigured to execute the operational instructions to: generate systemaddressing information for the plurality of encoded data slices based,at least in part, on an identifier associated with the data object. 9.The computing device of claim 8, wherein the processing module isfurther configured to execute the operational instructions to: update anentry of at least one of a directory or a dispersed hierarchical indexto associate the data object and the system addressing information. 10.A computing device comprising: memory including operationalinstructions; and a processing module operably coupled to the memory,the processing module configured to execute the operational instructionsto: receive a data object for storage in memory of a storage network;forward the data object for storage in a first set of storage nodes ofthe storage network, the first set of storage nodes including aplurality of memory devices comprised of a first memory type; determinea system level storage efficiency preference for the data object based,at least in part, on a data attribute associated with the data object;select, based at least in part on the system level storage efficiencypreference, a second set of storage nodes of the storage network, thesecond set of storage nodes including a plurality of memory devicescomprised of a second memory type; determine error encoding parametersbased, at least in part, on the second set of storage nodes; retrievethe data object from the first set of storage nodes; encode the dataobject in accordance with the error encoding parameters to generate aplurality of encoded data slices; and forward the plurality of encodeddata slices to the second set of storage nodes for storage therein. 11.The computing device of claim 10, wherein the data attribute is a datasize of the data object.
 12. The computing device of claim 10, whereinthe data attribute is a data type of the data object.
 13. The computingdevice of claim 10, wherein determining the system level storageefficiency preference for the data object is further based on systemstorage node information.
 14. The computing device of claim 10, whereindetermining the error encoding parameters is further based on the systemlevel storage efficiency preference.
 15. The computing device of claim10, wherein determining the error encoding parameters includesdetermining the error encoding parameters based on a number of storagenodes of the second set of storage nodes.
 16. A method for execution byone or more computing devices, the method comprising: receiving a dataobject for storage in memory of a storage network; forwarding the dataobject for storage in a first plurality of memory devices of the storagenetwork, the first plurality of memory devices comprised of a firstmemory type; determining a system level storage efficiency preferencefor the data object based, at least in part, on a data attributeassociated with the data object; selecting, based at least in part onthe system level storage efficiency preference, a second plurality ofmemory devices of the storage network, the second plurality of memorydevices comprised of a second memory type; determining error encodingparameters based, at least in part, on the second plurality of memorydevices; retrieving the data object from the first plurality of memorydevices; encoding the data object in accordance with the error encodingparameters to generate a plurality of encoded data slices; andforwarding the plurality of encoded data slices to the second pluralityof memory devices for storage therein.
 17. The method of claim 16,wherein the data attribute is a data size of the data object.
 18. Themethod of claim 16, wherein the data attribute is a data type of thedata object.
 19. The method of claim 16, wherein determining the errorencoding parameters is further based on the system level storageefficiency preference.
 20. The method of claim 16, wherein the firstplurality of memory devices is included in a first set of storage nodesof the storage network and the second plurality of memory devices isincluded in a second set of storage nodes of the storage network, andwherein determining the error encoding parameters includes determiningthe error encoding parameters based on a number of storage nodes of thesecond set of storage nodes.